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A STAM-LSTM model for wind power prediction with feature selection

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  • Cao, Wangbin
  • Wang, Guangxing
  • Liang, Xiaolin
  • Hu, Zhengwei

Abstract

In an effort to enhance the precision of wind power prediction, this study proposes a wind power prediction model with a secondary-weighted attention mechanism, which is based on feature selection. During the pre-processing stage, wavelet denoising is employed on the original dataset to eliminate noise and enhance the convergence rate of the model. As for model improvement, a secondary-weighted time attention mechanism-LSTM (STAM-LSTM) model is proposed. Additionally, the random forest algorithm is employed to analyse the feature correlation, leading to the best feature combination for the construction of the final input vector. In the comparative experiments, the STAM-LSTM model shows good performance and stability compared to the other nine models and three reference methods. In addition, to validate the effectiveness of feature selection, different combinations of features are entered for prediction. The results show that the model metrics reach a better level after feature selection. Finally, the effects of the STAM mechanism and the Random Forest feature screening assistance strategy, on the model are analysed through ablation experiments.

Suggested Citation

  • Cao, Wangbin & Wang, Guangxing & Liang, Xiaolin & Hu, Zhengwei, 2024. "A STAM-LSTM model for wind power prediction with feature selection," Energy, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:energy:v:296:y:2024:i:c:s0360544224008028
    DOI: 10.1016/j.energy.2024.131030
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    References listed on IDEAS

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    1. Bitencourt, Hugo Vinicius & de Souza, Luiz Augusto Facury & dos Santos, Matheus Cascalho & Silva, Rodrigo & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha, 2023. "Combining embeddings and fuzzy time series for high-dimensional time series forecasting in internet of energy applications," Energy, Elsevier, vol. 271(C).
    2. Tian, Chaonan & Niu, Tong & Wei, Wei, 2022. "Developing a wind power forecasting system based on deep learning with attention mechanism," Energy, Elsevier, vol. 257(C).
    3. Lu Jing-yi & Lin Hong & Ye Dong & Zhang Yan-sheng, 2016. "A New Wavelet Threshold Function and Denoising Application," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-8, May.
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